where \(\mathrm {Var}_{\mathbb {P}}(\ell (x, \xi )) := \mathbb {E}_{\mathbb {P}}((\ell (x, \xi ) - \mathbb {E}_{ \mathbb {P}}(\ell (x, \xi )) )^2)\)  is the variance of the cost under distribution \(\mathbb {P}\) .
SVP predictors were considered previously by [1]} as an alternative to naive ERM. Their consideration of SVP may be understood through the perspective of a classical “bias-variance” trade-off between minimizing an empirical risk and a variance-sensitive regularization term motivated by concentration inequalities such as the one found in Equation (REF ). We will show that also in our framework the SVP predictor will play a protagonist role in the subexponential regime.
